One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Comparison between different techniques of preprocessing for resting state fMRI analysis

Resting state functional connectivity as the name suggests is defined as significant temporal correlation between spatially distinct regions of the brain during rest. In this thesis, fMRI resting state dataset was analyzed using different available processing techniques with the same fMRI data to study differences between the various methods. All the imaging data from each of the subjects was processed in an identical fashion. The same method was used for detecting connectivity. The number of independent components in the data was used as the base to differentiate the effect of each of these methods. Independent component analysis was performed on each step after and before converting each dataset into MNI space to see the effect of normalization. In resting state fMRI study, different algorithms of motion correction showed no significant difference in the results. Temporal filtering by rectangular filter for particular bands of frequency showed no significant difference in the data analysis. Gaussian and Hamming windows however, work well for the required purpose. In case of spatial smoothing, Unsharp and Sobel filters which emphasize on the edges resulted in an abnormally high increase in number of components which suggested low pass filters like Gaussian and Average are more suitable for fMRI preprocessing

Magnetoencephalography

This is a practical book on MEG that covers a wide range of topics. The book begins with a series of reviews on the use of MEG for clinical applications, the study of cognitive functions in various diseases, and one chapter focusing specifically on studies of memory with MEG. There are sections with chapters that describe source localization issues, the use of beamformers and dipole source methods, as well as phase-based analyses, and a step-by-step guide to using dipoles for epilepsy spike analyses. The book ends with a section describing new innovations in MEG systems, namely an on-line real-time MEG data acquisition system, novel applications for MEG research, and a proposal for a helium re-circulation system. With such breadth of topics, there will be a chapter that is of interest to every MEG researcher or clinician

Numerical Simulation

Nowadays mathematical modeling and numerical simulations play an important role in life and natural science. Numerous researchers are working in developing different methods and techniques to help understand the behavior of very complex systems, from the brain activity with real importance in medicine to the turbulent flows with important applications in physics and engineering. This book presents an overview of some models, methods, and numerical computations that are useful for the applied research scientists and mathematicians, fluid tech engineers, and postgraduate students

Data-driven neural mass modelling

The brain is a complex organ whose activity spans multiple scales, both spatial and temporal. The computational unit of the brain is thought to be the neurone. At the microscopic level, neurones communicate via action potentials. These may be observed experimentally by means of precise techniques that work with a small number of these cells and their interactions, and that can be modelled mathematically in a variety of ways. Other techniques consider the averaged activity of large groups of neurones in the mesoscale, or cortical columns; theoretical models of these signals also abound. The problem of relating the microscopic scale to the mesoscopic is not trivial. Analytical derivations of mesoscopic models are based on assumptions that are not always justified. Also, traditionally there has been a separation between the clinically oriented analysts that process neural signals for medical purposes and the theoretical modelling community. This Thesis aims to lay bridges both between the microscopic and mesoscopic scales of brain activity, and between the experimental and theoretical angles of its study. This is achieved via the unscented Kalman filter (UKF), which allows us to combine knowledge from different sources (microscopic/mesoscopic and experimental/theoretical). The outcome is a better understanding of the system than each of the sources of information could provide separately. The Thesis is organised as follows. Chapter 1 is a brief reflection on the current methodology in Science and its underlying motivations. This is followed by chapters 2 to 4, which introduce and contextualise the concepts discussed in the remainder of the work. Chapter 5 tackles the interrelationship of the microscopic and mesoscopic scales. Although efforts have been made to derive mesoscopic equations from models of microscopic networks, they are based on assumptions that may not always hold. We use the UKF to assimilate the output of microscopic networks into a mesoscopic model and study a variety of dynamical situations. Our results show that using the Kalman filter compensates for the loss of information that is common in analytical derivations. Chapters 6 and 7 address the combination of experimental data with neural mass models. More specifically, we extend Jansen and Rit's model of a cortical column with a model of the head, which allows us to use electroencephalography (EEG) data. With this, we estimate the state of the system and a relevant parameter of choice. In chapter 6 we use in silico data to test the UKF under a variety of dynamical conditions, comparing simulated intracranial data with simulated EEG. Extracranial estimation is always superior in speed and quality to intracortical estimation, even though intracortical electrodes are closer to the source of activity than extracranial electrodes. We suggest that this is due to the more complete picture of the cortex that is visible with the set of extracranial electrodes. Chapter 7 feeds experimental EEG data of an epileptic patient into Jansen and Rit's model; the goal is to estimate a parameter that governs the dynamical behaviour of the system, again with the UKF. The estimation of the state closely follows the experimental data, while the parameter shows sensitivity to the changes in brain regimes, especially seizures. These results show promise for using data assimilation to address some shortcomings of brain modelling techniques. On the one hand, the mutual influence of neural structures at the microscopic and the mesoscopic scales may become better characterised, by means of filtering approaches that bypass analytical limitations. On the other hand, fusing experimental EEG data with mathematical models of the brain may enable us to determine the underlying dynamics of observed physiological signals, and at the same time to improve our models with patient-specific information. The potential of these enhanced algorithms spans a wide range of brain-related applications.El cervell humà és un òrgan de gran complexitat l’activitat del qual es desenvolupa en múltiples escales, tant espacials com temporals. Es creu que la unitat computacional del cervell és la neurona, una cèl·lula altament especialitzada que té com a funció rebre, processar i transmetre informació. A nivell microscòpic, les neurones es comuniquen les unes amb les altres per potencials d’acció. Aquests es poden observar experimentalment “in vivo” per mitjà de tècniques de gran precisió que només poden tenir en compte un nombre relativament reduït de cèl·lules i interaccions, i que es poden modelar matemàticament de diverses maneres. Altres tècniques tracten amb grans grups de neurones a escala mesoscòpica, o columnes corticals, i detecten l’activitat mitjana de la població neuronal; en aquest cas també abunden els models teòrics que intenten reproduir aquests senyals. Malgrat que està ben establert que hi ha una intercomunicació entre les escales microscòpica i mesoscòpica, relacionar una escala amb una altra no és gens trivial. Les derivacions analítiques de models mesoscòpics a partir de xarxes microscòpiques es basen en suposicions que no sempre es poden justificar. A part, tradicionalment hi ha hagut una frontera de separació entre els analistes clínics que processen senyals neuronals amb fins mèdics (i que sovint usen tècniques molt invasives i/o costoses), i la comunitat teòrica que modelitza aquests senyals, per a qui el repte més gran és caracteritzar els paràmetres que governen els models perquè aquests s’acostin el més possible a la realitat. Aquesta Tesi té com a objectiu, per una banda, fer un pas més a caracteritzar la relació entre les escales microscòpica i mesoscòpica d’activitat cerebral, i, per l’altra, establir ponts entre els punts de vista experimental i teòric del seu estudi. Ho aconseguim amb un algoritme d’assimilació de dades, el filtre de Kalman desodorat (UKF, de les sigles en anglès), que ens permet combinar informació de diverses procedències (microscòpica/mesoscòpica o experimental/teòrica). El resultat és una comprensió més àmplia del sistema estudiat que la que haurien permès les fonts d’informació per separat. La Tesi està organitzada de la següent manera. El capítol 1 comença amb una breu reflexió sobre la metodologia científica actual i les seves motivacions subjacents (segons l’autora). El segueixen els capítols del 2 al 4, que introdueixen i posen en context els conceptes que s’exposen a la resta del treball. El capítol 5 aborda el problema de la relació entre l’escala microscòpica i la mesoscòpica. Tot i que existeixen diverses derivacions d’equacions mesoscòpiques partint de models de xarxes neuronals, sovint es basen en suposicions fràgils que no es compleixen en situacions més complicades. Aquí utilitzem l’UKF per assimilar la sortida de xarxes microscòpiques en un model mesoscòpic simple i estudiar diverses situacions dinàmiques. Els resultats mostren que la manera que el filtre de Kalman gestiona les incerteses del model compensa les pèrdues d’informació pròpies de les derivacions analítiques de models mesoscòpics. Els capítols 6 i 7 tracten la combinació de dades experimentals del cervell amb models de masses neurals que descriuen la dinàmica de grups de neurones. Concretament, estenem el model de Jansen i Rit d’una columna cortical amb un model del cap, el qual ens permet fer servir dades extracranials no invasives. Amb això estimem l’estat del sistema i un paràmetre d’interès de possible rellevància en l’estudi clínic d’afeccions com l’epilèpsia. En el capítol 6 fem servir dades “in silico” per provar l’UKF en diversos escenaris dinàmics: conjunts de paràmetres que causen comportaments diferents en les columnes corticals, diferents nivells de soroll de mesura i dues modalitats de transmissió d’informació; tot això comparant dades intracranials simulades amb simulacions d’electroencefalogrames (EEG). En totes les situacions estudiades, l’estimació extracranial és sempre superior, en velocitat i precisió, a l’estimació intracortical, encara que els elèctrodes intracorticals són molt més propers a la font de l’activitat que els elèctrodes de la superfície cranial. Suggerim que això pot ser causat per la visió més completa del còrtex que es pot obtenir amb el conjunt d’elèctrodes extracranials. Aquesta idea ve reforçada pels resultats observats amb elèctrodes extracranials individuals treballant de manera independent, que apunten a la sensibilitat espacial de les mesures. En el capítol 7 alimentem el model de Jansen i Rit amb dades experimentals de l’EEG d’un pacient epilèptic; l’objectiu és estimar un paràmetre significatiu que governa l’evolució dinàmica del sistema, de nou amb l’UKF. L’estimació de l’estat és precisa i el paràmetre es veu afectat pels canvis de règim, especialment (però no exclusivament) per les convulsions. Aquests resultats són prometedors a l’hora d’utilitzar l’assimilació de dades per superar les diverses carències de les tècniques de modelització cerebral. Per una banda, la influència mútua entre estructures a escala microscòpica i a escala mesoscòpica es pot caracteritzar millor, gràcies a tècniques de filtrat que permeten esquivar les habituals limitacions analítiques. Això dóna com a resultat una millor comprensió de l’estructura i funció cerebrals. Per una altra banda, fusionar dades experimentals d’EEG amb els models matemàtics del cervell existents ens pot permetre determinar les dinàmiques subjacents dels senyals fisiològics que tenim disponibles, a la vegada que millorem els nostres models amb informació individual de cada pacient. Aquests algoritmes augmentats tenen potencial per a un ampli espectre d’aplicacions en el camp de les neurociències, des d’interfícies cervell/ordinador fins a tota mena d’usos en medicina personalitzada com el diagnòstic precoç de malalties neurodegeneratives, la predicció de crisis convulsives o la monitorització de la rehabilitació postisquèmica o posttraumàtica, entre molts altres.Postprint (published version

Predictability of epileptic seizures by fusion of scalp EEG and fMRI

The systems for prediction of epileptic seizure investigated in recent years mainly rely on the traditional nonlinear analysis of the brain signals from intracranial electroencephalograph (EEG) recordings. The overall objective of this work focuses on investigation of the predictability of seizure from the scalp signals by applying effective blind source separation (BSS) techniques to scalp EEGs, in which the epileptic seizures are considered as independent components of the scalp EEGs. The ultimate goal of the work is to pave the way for epileptic seizure prediction from the scalp EEG. The main contributions of this research are summarized as follows. Firstly, a novel constrained topographic independent component analysis (CTICA) algorithm is developed for the improved separation of the epileptic seizure signals. The related CTICA model is more suitable for brain signal separation due to the relaxation of the independence assumption, as the source signals geometrically close to each other are assumed to have some dependencies. By incorporating the spatial and frequency information of seizure signals as the constraint, CTICA achieves a better performance in separating the seizure signals in comparison with other conventional ICA methods. Secondly, the predictability of seizure is investigated. The traditional method for quantification of the nonlinear dynamics of time series is employed to quantify the level of chaos of the estimated sources. The simultaneously recorded intracranial and scalp EEGs are used for the comparison of the results. The experiment results demonstrate that the separated seizure sources have a similar transition trend as those achieved from the intracranial EEGs. Thirdly, simultaneously recorded EEG and functional Magnetic Resonance Imaging (fMRI) is studied in order to validate the activated area of the brain related to the seizure sources. An effective method to remove the fMRI scanner artifacts from the scalp EEG is established by applying the blind source extraction (BSE) algorithm. The results show that the effect of fMRI scanner artifacts has been reduced in scalp EEG recordings. Finally, a data driven model, spatial ICA (SICA) subject to EEG as the temporal constraint is proposed in order to detect the Blood Oxygen-Level Dependence (BOLD) from the seizure fMRI. In contrast to the popular model driven method General Linear Model (GLM), SICA does not rely on any predefined hemodynamic response function. It is based on the fact that brain areas executing different tasks are spatially independent. Therefore SICA works perfectly for non-event-related fMRI analysis such as seizure fMRI. By incorporating the temporal information existing within the EEG as the constraint, the superiority of the proposed constrained SICA is validated in terms of better algorithm convergence and a higher correlation between the time courses of the component and the seizure EEG signals as compared to SICA

Proceedings of Abstracts, School of Physics, Engineering and Computer Science Research Conference 2022

© 2022 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Plenary by Prof. Timothy Foat, ‘Indoor dispersion at Dstl and its recent application to COVID-19 transmission’ is © Crown copyright (2022), Dstl. This material is licensed under the terms of the Open Government Licence except where otherwise stated. To view this licence, visit http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected] present proceedings record the abstracts submitted and accepted for presentation at SPECS 2022, the second edition of the School of Physics, Engineering and Computer Science Research Conference that took place online, the 12th April 2022

Traction force microscopy with optimized regularization and automated Bayesian parameter selection for comparing cells

Adherent cells exert traction forces on to their environment, which allows them to migrate, to maintain tissue integrity, and to form complex multicellular structures. This traction can be measured in a perturbation-free manner with traction force microscopy (TFM). In TFM, traction is usually calculated via the solution of a linear system, which is complicated by undersampled input data, acquisition noise, and large condition numbers for some methods. Therefore, standard TFM algorithms either employ data filtering or regularization. However, these approaches require a manual selection of filter- or regularization parameters and consequently exhibit a substantial degree of subjectiveness. This shortcoming is particularly serious when cells in different conditions are to be compared because optimal noise suppression needs to be adapted for every situation, which invariably results in systematic errors. Here, we systematically test the performance of new methods from computer vision and Bayesian inference for solving the inverse problem in TFM. We compare two classical schemes, L1- and L2-regularization, with three previously untested schemes, namely Elastic Net regularization, Proximal Gradient Lasso, and Proximal Gradient Elastic Net. Overall, we find that Elastic Net regularization, which combines L1 and L2 regularization, outperforms all other methods with regard to accuracy of traction reconstruction. Next, we develop two methods, Bayesian L2 regularization and Advanced Bayesian L2 regularization, for automatic, optimal L2 regularization. Using artificial data and experimental data, we show that these methods enable robust reconstruction of traction without requiring a difficult selection of regularization parameters specifically for each data set. Thus, Bayesian methods can mitigate the considerable uncertainty inherent in comparing cellular traction forces